2  Breakeven Points and Tax Bill Distributions

Steps for prepping data for graphs below:

Bills were pulled from PTAXSIM amd summed to the pin level in files 1_...rmd and 2...rmd. Exemption data per PIN was pulled from PTAXSIM in file 3_.....rmd.

Code

ptaxsim_db_conn <- DBI::dbConnect(RSQLite::SQLite(), "../ptaxsim.db/ptaxsim-2021.0.4.db")

eq_factor <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  "SELECT*
  FROM eq_factor
  "
)

muni_agency_names <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  "SELECT DISTINCT agency_num, agency_name, minor_type
  FROM agency_info
  WHERE minor_type = 'MUNI'
  OR agency_num = '020060000'  

  "
)

muni_tax_codes <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  glue_sql("
  SELECT DISTINCT agency_num, tax_code_num
  FROM tax_code
  WHERE agency_num IN ({muni_agency_names$agency_num*})
  AND year = 2021
  ",
  .con = ptaxsim_db_conn
  )
) %>% 
  mutate(tax_code_num = as.numeric(tax_code_num))

tax_codes <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  glue_sql("
  SELECT DISTINCT tax_code_num, tax_code_rate
  FROM tax_code
  WHERE year = 2021
  ",
  .con = ptaxsim_db_conn
  )
) %>% mutate(tax_code_num = as.numeric(tax_code_num))

Code

taxbills_current <- read_csv("../Output/Dont_Upload/1_Get_All_Pins-CookPinTaxbills_2021_Actual.csv")
# 22,453,875 tax bills in 2021 in municipalities. 
# taxing agency-PIN is the unit of observation here (only partial property tax bills)
# Each PIN has multiple rows (because it is taxed by multiple taxing agencies)

sapply(taxbills_current, function(x) sum(is.na(x)))


# 1,825,816 billed properties with 14-digit PINs in incororated areas
# 1,864,594 in incorported and unincorporated areas.
pin14_bills_current <- taxbills_current %>%
  group_by(tax_code, class, pin) %>%
  
  mutate(total_bill = final_tax_to_dist + final_tax_to_tif) %>% # from each taxing agency
  
  summarize(
    total_billed = sum(total_bill, na.rm = TRUE), # total on someone's property tax bill
    av = first(av),
    eav = first(eav),
    # pin_count_in_parcel = n(),
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE),
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), # revenue lost due to exemptions
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), # total rev before all exemptions
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE), # total rev after all exemptions
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  )  %>% 
  mutate(propclass_1dig = str_sub(class, 1, 1))

# head(pin14_bills_current)

sapply(pin14_bills_current, function(x) sum(is.na(x)))


# Now group again and sum values for tax code and each property class!

# There are 28,381 property class - tax code group combinations in incorporated areas.
# There are 28,534 property class - tax code group combos in incorporated + unincorporated areas

rm(taxbills_current)

Need to merge PIN level data of tax bills and exemptions per PIN if the goal is to calculate new tax rates for different scenarios AND view individual level tax bill changes. (i.e. if we want to look at the “median taxpayer” then need to use this method).

Note: The EAV from the exemption table is the original EAV (AV * equalizer) without consider TIFs or exemptions.

31,000 properties have $0 tax bills based on exemption data table made from PTAXSIM’s pin table. (Using the tax bill data allows for more in depth look at tax bills and can create variables such as small_bill if bills were less than $100 after exemptions were applied).

Code

pin_eav <- read_csv("../Output/Dont_Upload/3_Exemption_Details_output-all_cook_pin_exemptions_2021_actual.csv") %>%
  #filter(class_code !=0) %>%
  
  mutate(all_exemptions = exe_homeowner + exe_senior + exe_freeze + exe_longtime_homeowner + 
           exe_disabled + exe_vet_returning + exe_vet_dis_lt50 + exe_vet_dis_50_69 + exe_vet_dis_ge70 + exe_abate) %>%
  mutate(zero_bill = ifelse(eav <= all_exemptions, 1, 0),
         has_HO_exemp = ifelse(exe_homeowner > 0, 1, 0)) %>%
  rename(equalized_AV = eav,
         pin_AV = av)

# table(pin_eav$zero_bill)

table(pin_eav$major_class_code, pin_eav$zero_bill)

# table(pin_eav$major_class_code, pin_eav$has_HO_exemp)



## Add exemption types and values to the tax bill data at PIN level
pin_data <- left_join(pin14_bills_current, pin_eav, by = c("pin", "class" = "class_code" ))

## Add tax code tax rate to PIN level data
pin_data <- left_join(pin_data, tax_codes, by = c("tax_code" = "tax_code_num"))


taxcodes_current <- pin_data %>% 
  group_by(tax_code)  %>%
  summarize(
    av = sum(av),
    eav = sum(eav),
    equalized_AV = sum(equalized_AV),
    pins_in_class = n(),
    current_exemptions = sum(all_exemptions),
    HO_exemps = sum(exe_homeowner),
    tax_code_rate = first(tax_code_rate), 
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) %>%
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) %>%
  rename(cur_comp_TC_rate = tax_code_rate) %>%
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100),
         new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100) + HO_exemps) %>%
  mutate(new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav)*100) %>%
  mutate(new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate)) %>%
  select(tax_code, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, everything())


#head(taxcodes_current) 


taxcode_taxrates <- taxcodes_current %>% 
  select(tax_code, cur_comp_TC_rate, new_comp_TC_rate, current_exemptions, HO_exemps)

pin_data2 <- left_join(pin_data, taxcode_taxrates, by = c("tax_code" = "tax_code"))


pin_data2 <- pin_data2 %>% left_join(muni_tax_codes) 

pin_data2 <- pin_data2 %>% left_join(muni_agency_names)

# write_csv(pin_data2, "../Output/4C_joined_PINs_bills_and_exemptions.csv")

Calculate composite tax rates at municipal level.
- new_taxable_eav is is the current taxable EAV + the GHE exempt EAV. The taxrates calculated in this file are for GHE removal only. Other exemption types are still assumed to exist.

The assessed value and original equalized assessed values come from the pin data table within PTAXSIM. This table also has every type of exemption that the property received and the amount of EAV that was exempt due to the exemption.

Download 4C_joined_PINs from Box link here:. I would move the downloaded file into your R project and then the code below should work without changing the file path.

Code

pin_data2 <- read_csv("../Output/4C_joined_PINs_bills_and_exemptions.csv")

pin_data2 <- pin_data2 %>% 
  mutate(
    bill_current = (final_tax_to_dist + final_tax_to_tif),
    bill_noGHE = new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
    bill_noexemps = new_comp_TC_rate/100*(equalized_AV),
    bill_change = bill_noGHE - bill_current)

Code

C2_munistats <- pin_data2 %>% 
  filter(class > 199 & class < 300) %>%
  group_by(agency_name)  %>%
  arrange(av) %>%
  summarize(
    median_eav = round(median(eav)), 
    median_av = round(median(av)), 
    avg_av = round(mean(av)),
    avg_eav = round(mean(eav)),
    C2_pins_in_muni = n(),
    C2_current_exemptions = sum(all_exemptions, na.rm = TRUE),
    C2_HO_exemps = sum(exe_homeowner, na.rm = TRUE),
  ) 

C2_munistats%>% left_join(nicknames) %>%
  select(clean_name, everything()) %>% select(-c(agency_name, agency_number))

Table 2.1:

Measure of the middle for Major Class 2 Properties

Code

## removes properties that have more than one exemption type
C2_munistats_filtered <- pin_data2 %>% 
  filter(class > 199 & class < 300) %>%
    filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0) %>%
  group_by(agency_name)  %>%
  arrange(av) %>%
  summarize(
    median_eav = round(median(eav, na.rm=TRUE)), 
    median_av = round(median(av, na.rm=TRUE)), 
    avg_av = round(mean(av, na.rm=TRUE)),
    avg_eav = round(mean(eav, na.rm=TRUE)),
    C2_pins_in_muni = n(),
    C2_current_exemptions = sum(all_exemptions, na.rm = TRUE),
    C2_HO_exemps = sum(exe_homeowner, na.rm = TRUE),
  ) 
C2_munistats_filtered %>% left_join(nicknames) %>%
  select(clean_name, everything()) %>% select(-c(agency_name, agency_number))

Table 2.2: Measure of the middle for Major Class 2 Properties

(a)

Calculates statistics from PINs that remain after removing properties that received other exemption types. Median and mean statistics for Class 200 - 299 properties (i.e. Major Class 2 Residential Properties

677,602 residential properties have the GHE exemption and no other exemption types. 834,795 properties (residential and nonresidential) do not have exemption types.

around 350,000 PINs have at least one other exemption type that is not the GHE.

Calculate the composite tax rate (current and hypothetical from eliminating GHE) and merge in other key variables to the pin level data:

2.0.1 Calculating composite tax rates

current tax rate = aggregate levy for all taxing agencies within the geography being used for comparison / taxable EAV

\(\frac{Levy}{EAV_{Taxable}} = TaxRate\)

\(current\_taxable\_eav = \frac{final\_tax\_to\_dist}{(\frac{(cur\_comp\_muni\_rate)}{100})}\)

taxable EAV = non-TIF increment EAV after exempt EAV has been subtracted

Code

eq_2021 <- 3.0027


muni_taxrates <- pin_data2 %>% 
  ungroup() %>%
  group_by(agency_name)  %>%
  arrange(av) %>%
  
  summarize(
    muni_median_av = round(median(av, na.rm=TRUE)),
    muni_median_eav = round(median(eav, na.rm=TRUE)),
    muni_mean_av = round(mean(av, na.rm=TRUE)),
    muni_mean_eav = round(mean(eav, na.rm=TRUE)),
    av = sum(av, na.rm = TRUE),
    eav = sum(eav, na.rm = TRUE),
    equalized_AV = sum(equalized_AV, na.rm = TRUE),
    pins_in_muni = n(),
    current_exemptions = sum(all_exemptions, na.rm = TRUE),
    HO_exemps = sum(exe_homeowner, na.rm = TRUE),
    tax_code_rate = mean(tax_code_rate, na.rm = TRUE), # Changed from first() to mean() on Nov 1
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) %>%
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) %>%
  rename(cur_comp_muni_rate = tax_code_rate) %>%
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_muni_rate/100),
         new_taxable_eav = final_tax_to_dist/(cur_comp_muni_rate/100) + HO_exemps) %>%
  mutate(new_comp_muni_rate = (final_tax_to_dist / new_taxable_eav)*100) %>%
  
  mutate(new_comp_muni_rate = ifelse(is.nan(new_comp_muni_rate), cur_comp_muni_rate, new_comp_muni_rate)) %>% 
  
  left_join(C2_munistats) %>%
  left_join(nicknames) %>%
  mutate(rate_change = cur_comp_muni_rate - new_comp_muni_rate,
        # nobillchange_propertyEAV = round(10000 * ((cur_comp_muni_rate/100) / (rate_change/100))),
        # nochange_av = round(nobillchange_propertyEAV / eq_2021),
        # nochange_ratio = nochange_av / median_av
        ) %>%
  select(clean_name, C2median_av = median_av, muni_median_av,  C2mean_av = avg_av, muni_mean_av, cur_comp_muni_rate, new_comp_muni_rate, current_taxable_eav, new_taxable_eav, everything())

muni_taxrates 

Code

#muni_taxrates %>% write_csv("../Output/4C_muni_taxrates.csv")

muni_taxrates %>% left_join(nicknames) %>% select(clean_name, muni_median_av, C2median_av )

have to also filter for properties that claimed the full exemption amount otherwise get multiple “breakeven points”

Why are there PINs that have $0 paid to nonTIF agencies and tax bills that go completely to TIF agencies?

Code

pin_data2 %>%
  group_by(agency_name) %>%
  summarize(min_rate = min(tax_code_rate),
            max_rate = max(tax_code_rate),
            mean_rate = mean(tax_code_rate),
            rate_range = max_rate - min_rate,
        )%>%
  left_join(nicknames) %>%
  select(clean_name, rate_range, min_rate, max_rate, mean_rate) %>%
  arrange(-rate_range)

2.0.2 Calculating breakeven points

The “breakeven point” is where the current taxbill for a property is the same amount as it would be in the hypothetical scenario. In this case, the hypothetical involves eliminating the GHE, which adds all of the exe_homeowner to the taxable EAV and decreases the tax rate.

hypothetical tax bill = current tax bill

EAV * tax rate w/o the GHE = EAV * current tax rate

\[ EAV * \tau_{wo} = EAV - exemptEAV_{homeowner} * \tau_w \]

which simplifies to:

\[ EAV_{breakeven} = - (\frac{\tau_w}{\tau_{wo} - \tau_w} * exemptEAV_{homeowner}) \]

\[ tax code tax rate = \frac{\sum{final\_tax\_to\_dist}}{\sum{CurrentTaxableEAV}} \]

Aggregate Municipal Levy \(\approx\) \(\sum{final\_tax\_to\_dist}\) because it is the amount billed by the taxing agency. It technically is based on the amount billed.

Homeowners think they are saving the equivalent of their exempt EAV * their current tax rate. This amount also appears on the tax bill and is the variable tax_amt_exe in the PTAXSIM database.

\(\sum{tax\_amt\_exe} = EAV_{exempt}*\tau_{current}\)

\(naive\_cost = \sum(bill\_change)\)

But if the EAV that was exempt became taxable, everyone’s tax rate would decrease to some extent. The change in tax rate varies on a tax code to tax code level and depends on the amount of EAV that becomes taxable in comparison to total EAV within the tax code.

For the GHE scenarios, the hypothetical tax rate and amount of EAV that is exempt changes in each scenario.

Perceived vs. Actual Savings: Homeowners think they are saving \(EAV_{exempt}* taxrate_{current} = \sum{tax\_amt\_exe}\) but their taxable EAV would be multiplied by a lower tax rate due to the increase in taxable EAV within the geographic region.

At the county level, the “cost” of exemptions is \$1.385 billion.¹

Code

muni_breakeven_points <- pin_data2 %>%  
  ungroup() %>%
  filter(class > 199 & class < 300) %>% 
  #filter(final_tax_to_tif == 0) %>%
  filter(exe_homeowner == 10000) %>% 
    filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
             exe_vet_dis_ge70 == 0 & 
             exe_abate == 0) %>%
  select(av, tax_code:all_exemptions, cur_comp_TC_rate,new_comp_TC_rate, agency_name) %>%
  left_join(muni_taxrates, by = "agency_name") %>% 
  group_by(clean_name) %>%
  summarize(median_AV = median(pin_AV),
            bill_current = mean(final_tax_to_dist.x), # money needed by non-TIF agencies
            # bill_noexemps =  new_comp_muni_rate/100*(equalized_AV.x-all_exemptions+exe_homeowner),
            bill_noGHE = mean(new_comp_TC_rate/100 * (equalized_AV.x-all_exemptions+exe_homeowner)), # this uses tax code tax rate
            bill_change = round(mean(bill_noGHE - bill_current)),
        
    rate_change_muni = mean(cur_comp_muni_rate - new_comp_muni_rate), # muni level, not tax code level
    rate_change_TCs = mean(cur_comp_TC_rate - new_comp_TC_rate),
    nobillchange_propertyEAV = round(mean(10000 * ((cur_comp_muni_rate/100) / (rate_change/100)))),
   nobillchange_propertyEAV_munirates = round(mean(exe_homeowner * ((cur_comp_muni_rate/100) / (rate_change_muni/100)))),
    nochange_av_munirates = round(mean(nobillchange_propertyEAV_munirates / eq_2021)),
   # rate_change_TC = cur_comp_TC_rate - new_comp_TC_rate, # tax code level
   # nobillchange_propertyEAV_TCrates = round(exe_homeowner * ((cur_comp_TC_rate/100) / (rate_change_TC/100))),
# nochange_av_TCrates = round(nobillchange_propertyEAV_TCrates / eq_2021),
    ratio_av_to_breakeven = mean(median_AV / nochange_av_munirates)    
 ) %>%
  #filter(between(bill_change, -5, 5))  %>% 
  select(clean_name, median_AV, ratio_av_to_breakeven, bill_change, nobillchange_propertyEAV_munirates, nochange_av_munirates, rate_change_muni, rate_change_TCs)

2.1 Assessed Value PIN distribution

Code

pin_data2 %>%
  filter((class > 199 & class < 300) & agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF RIVERDALE", "VILLAGE OF WINNETKA")) %>%
  group_by(agency_name) %>%
  ggplot() +
  geom_histogram(aes(x = av, bins = 100)) +
#  geom_vline(aes(xintercept = median(av, na.rm=TRUE), color = "red")) +
  theme_classic()+
  theme(legend.position = "none") + 
 # geom_text(aes(x=(median(av) + 5000), y = 1500, label = median(av))) +
  scale_x_continuous(limits = c(-5, 50000)) +
  #   scale_y_continuous(limits = c(0, 1500))+
  labs(title = "PIN AV Distribution for All Class 2 Properties", x="AV", y="Number of PINs") +
  facet_wrap(~agency_name)

Code

pin_data2 %>%
  filter((class > 199 & class < 300) &  agency_name == "VILLAGE OF PHOENIX") %>%
  group_by(agency_name) %>%
  ggplot() +
  geom_histogram(aes(x = av, bins = 100)) +
  geom_vline(aes(xintercept = median(av, na.rm=TRUE), color = "red")) +
  geom_vline(aes(xintercept = 7974), linetype = "dashed", label = "Breakeven AV") +
  theme_classic()+
  theme(legend.position = "none") + 
  geom_text(aes(x=(median(av)), y = 450, label = median(av))) +
  geom_text(aes(x=14000, y = 350), label = "Breakeven AV = $7,974") +
  scale_x_continuous(limits = c(-5, 50000)) +
  #   scale_y_continuous(limits = c(0, 1500))+
  labs(title = "Phoenix PIN AV Distribution for All Class 2 Properties", x="AV", y="Number of PINs",
      caption =  "Median AV is $3,502, Breakeven AV is $7,974")

Code

pin_data2 %>%
  filter((class > 199 & class < 300) &  agency_name == "CITY OF CHICAGO") %>%
  ggplot() +
  geom_histogram(aes(x = av, bins = 100)) +
  geom_vline(aes(xintercept = median(av, na.rm=TRUE), color = "red")) +
  geom_vline(aes(xintercept = 84228), linetype = "dashed") +
  theme_classic()+
  theme(legend.position = "none") + 
  geom_text(aes(x=median(av)+20000, y = 300000, label = median(av))) +
 geom_text(aes(x=100000, y = 200000), label = "Breakeven AV = $84,228") +

  scale_x_continuous(limits = c(-5, 500000), labels = scales::dollar) +
  scale_y_continuous(limits = c(0, 300000))+
  labs(title = "Chicago PIN AV Distribution for All Class 2 Properties", 
       x="AV", y="Number of PINs",
      caption =  "Median AV is ~$21,000, Breakeven AV is ~$84,000") 

2.1.1 How many homeowners would have decreased taxbills if the GHE were eliminated?

Code

pin_data2 %>% 
  filter((class > 199 & class < 300) ) %>%  
  left_join(muni_taxrates, by = "agency_name") %>%
  left_join(muni_breakeven_points, by = "clean_name") %>%
  filter(pin_AV > nochange_av_munirates) %>%
  group_by(clean_name, has_HO_exemp) %>%
  summarize(n()) %>%
  mutate(has_HO_exemp = ifelse(has_HO_exemp == 0, "Did not claim", "Claimed GHE"))

Table 2.3:

Count of PINs with AV > breakeven point. Property classes 200-299.

Code

pin_data2 %>% 
  filter((class > 199 & class < 300)  & (has_HO_exemp == 1)) %>%
  left_join(muni_taxrates, by = "agency_name") %>%
    left_join(muni_breakeven_points, by = "clean_name") %>%

  filter(pin_AV > nochange_av_munirates) %>%
  group_by(agency_name) %>%
  summarize(pins_over_breakpoint = n()) %>%
  left_join(muni_taxrates) %>%
  mutate(pct_c2_benefits = round((pins_over_breakpoint / C2_pins_in_muni)*100, digits = 4)) %>%
  select(clean_name, pins_over_breakpoint, pct_c2_benefits)

Table 2.4:

Count of PINs with AV > breakeven point. Property classes 200 - 299 that claimed GHE in 2021

Code

pin_data2 %>% 
  filter((class > 199 & class < 300) & has_HO_exemp == 0) %>%
  left_join(muni_taxrates, by = "agency_name") %>%
  group_by(agency_name) %>%
  summarize(lowerbill_PINcount = n()
            ) %>%
  left_join(muni_taxrates) %>%
  mutate(pct_c2_benefits = round((lowerbill_PINcount / C2_pins_in_muni)*100, digits = 4)) %>%
  select(clean_name, lowerbill_PINcount, pct_c2_benefits) %>% arrange(pct_c2_benefits)

Table 2.5:

Count of PINs with AV > breakeven point. Property classes 200 - 299 that did not claim the GHE in 2021

Code

pin_data2 %>% 
  filter(class > 199 & class < 300) %>%
  left_join(muni_taxrates, by = "agency_name") %>%
    left_join(muni_breakeven_points, by = "clean_name") %>%

  filter(pin_AV < nochange_av_munirates & has_HO_exemp == 1) %>%
  group_by(agency_name) %>%
  summarize(under_breakpoint = n()) %>%
  left_join(muni_taxrates) %>%
  mutate(pct_c2_w_higherbills = round((under_breakpoint / C2_pins_in_muni)*100, digits = 4)) %>%
  select(clean_name, under_breakpoint, pct_c2_w_higherbills) %>% arrange(clean_name)

Table 2.6:

Count of PINs that claimed GHE with AV below the breakeven point. These properties would experience an increased tax bill if the GHE were removed.

Code

homeowners_higherbills <- pin_data2 %>%   
  filter(class > 199 & class < 300) %>%
  filter((cur_comp_TC_rate*(eav-exe_homeowner) < (new_comp_TC_rate*eav))) %>%
  group_by(agency_name, ) %>%
  summarize(higherbill_PINcount = n()) %>%
  left_join(muni_taxrates, by = "agency_name") %>%
  select(clean_name, higherbill_PINcount, C2_pins_in_muni) %>%
  mutate(pct_C2_higherbills = higherbill_PINcount / C2_pins_in_muni)
homeowners_higherbills %>% arrange(clean_name)

Table 2.7:

Count of PINs where current tax bill < hypothetical tax bill

Code

## Number of residential PINs that would have a lower tax bill in each muni
homeowners_benefited <- pin_data2 %>% 
  filter(class > 199 & class < 300) %>%
  # filter keeps obs if they didn't claim the exemption OR they did claim it & have fancy houses
  # filter(has_HO_exemp == 0 | (has_HO_exemp == 1 & pin_AV > nochange_av)) %>%
  
  filter(
    # did not claim the general homeowner exemption (GHE)
    has_HO_exemp == 0 | 
      # OR 
      # did claim the GHE in 2021, but their new bill still goes down compared 
      # to than their current tax bill if the GHE were removed  
      # implies that the hypothetical taxrate change*eav > exempt EAV * current tax rate 
      (has_HO_exemp == 1 & (cur_comp_TC_rate*(eav-exe_homeowner) > (new_comp_TC_rate*eav)))) %>%
  
  group_by(agency_name, has_HO_exemp) %>%
  summarize(lowerbill_PINcount = n()) %>%
  left_join(muni_taxrates, by = "agency_name") %>%

  mutate(lowerbill_PINcount = ifelse(is.na(lowerbill_PINcount), 0, lowerbill_PINcount)) %>%
  pivot_wider(id_cols = c(clean_name, C2_pins_in_muni), names_from = has_HO_exemp, values_from =  lowerbill_PINcount,  ) %>% 
  rename(didnot_claim = `0`,
         lowerbill_did_claim_GHE = `1`) %>% 
  mutate(lowerbill_did_claim_GHE = ifelse(is.na(lowerbill_did_claim_GHE), 0, lowerbill_did_claim_GHE),
    lowerbill_PINcount = didnot_claim + lowerbill_did_claim_GHE,
    pct_c2_w_higherbills = 100-round((lowerbill_PINcount / C2_pins_in_muni)*100, digits = 2),
    pct_c2_w_lowerbills = round((lowerbill_PINcount / C2_pins_in_muni)*100, digits = 2),
    pct_c2_lowerbills_did_claim = round((lowerbill_did_claim_GHE / C2_pins_in_muni)*100, digits = 2),
    pct_c2_lowerbills_didnotclaim = round((didnot_claim / C2_pins_in_muni)*100, digits = 2))


# homeowners_benefited

Code

homeowners_benefited %>% arrange(clean_name)
homeowners_higherbills %>% arrange(clean_name)
#homeowners_benefited %>% write_csv("../Output/homeowner_billchanges.csv")

Comparison of increased and decreased tax bills

2.1.2 Property Value where Bill doesn’t change

Code

nobillchange <- muni_taxrates %>% 
  left_join(muni_breakeven_points, by = "clean_name") %>%
  rename(nobillchange_propertyEAV = nobillchange_propertyEAV_munirates,
         nochange_av = nochange_av_munirates)

nobillchange %>%       
  filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF RIVERDALE", "VILLAGE OF WINNETKA", "CITY OF CHICAGO")) %>%
  ggplot(aes(y=C2median_av, x = agency_name)) +
  geom_col()+
    geom_text(aes(y=C2median_av + 3000, label = round(C2median_av) ) ) +
  scale_y_continuous(labels = scales::dollar) +
  theme_classic() +
  scale_x_discrete(label = c("Chicago","Park Forest", "Phoenix", "Riverdale", "Winnetka")) + 
  labs(y = "Median Residential AV", x = "", title = "Median Residential Property Assessed Value - All Class 2 property Types")
nobillchange %>%       
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF RIVERDALE", "VILLAGE OF WINNETKA")) %>%
  ggplot(aes(y=(nobillchange_propertyEAV/eq_2021), x = agency_name)) +
  geom_col() +
  geom_text(aes(y=(nobillchange_propertyEAV/eq_2021) + 3000, label = round(nobillchange_propertyEAV/eq_2021)) ) +
  scale_y_continuous(labels = scales::dollar) +
  theme_classic() +
  scale_x_discrete(label = c("Chicago", "Park Forest", "Phoenix", "Riverdale", "Winnetka")) + 
  labs(y = "Breakeven Point - AV", x = "", title = "Residential Property AV Breakeven Point", caption = "Residential properties above these values would have their bills decrease if the GHE were eliminated
       (if they had claimed the GHE before)")

Code

nochange <- C2_munistats %>% 
  left_join(muni_taxrates) %>%
  left_join(muni_breakeven_points) %>%
  rename(nobillchange_propertyEAV = nobillchange_propertyEAV_munirates,
         nochange_av = nochange_av_munirates)

nochange %>%
 # filter(nochange_av < 300000) %>%
  ggplot(aes(y=nochange_av, 
             x = C2median_av, 
             label=clean_name
             )) +
  geom_point(aes(alpha = .5)) +
 # geom_smooth(method = "lm" )+
  geom_text(aes(y = (nochange_av-5000), x = (C2median_av)), size = 2)+
  theme_classic() +
  scale_x_continuous(labels = scales::dollar) +
  scale_y_continuous(labels = scales::dollar) +
  theme(legend.position = "none")+
  labs(y = " Breakeven Point", 
       x = "Median AV - Class 2 Properties in Municipality", title = "Some highly valued homes would have lower tax bills if the GHE were eliminated", 
       caption = "The breakeven point is the assessed values at which a major class 2 
       property would not have their taxbill change if the GHE were eliminated.")

Figure 2.1: Includes all municipalities

Code

nochange %>%
  filter(nochange_av < 300000) %>%
  ggplot(aes(y=nochange_av, 
             x = C2median_av, 
             label=clean_name
             )) +
  geom_point(aes(alpha = .5)) +
  
  geom_point(data = (nochange %>% filter(clean_name %in% c("Park Forest", "Chicago", "Winnetka", "Riverdale","Dolton"))), aes(y = nochange_av, x = C2median_av, color = "red"), size = 3) +
  ggrepel::geom_label_repel(data = (nochange %>% filter(clean_name %in% c("Park Forest", "Chicago", "Winnetka", "Riverdale", "Dolton"))), aes(y = (nochange_av), x = (C2median_av)), size = 3)+
  theme_classic() +
  scale_x_continuous(labels = scales::dollar) +
  scale_y_continuous(labels = scales::dollar) +
    theme(legend.position = "none") +

  labs(title = "Municipalities' Median AV & Breakeven Point - Class 2 Properties Only",
    y = "Breakeven Point", 
       x = "Median AV")

Figure 2.2: Excludes outliers: labeled

Code

nochange %>%
  filter(nochange_av < 300000) %>%
  ggplot(aes(y=nochange_av, 
             x = C2median_av, 
             label=clean_name
             )) +
  geom_point(aes(alpha = .5)) +
  
  geom_point(data = (nochange %>% filter(clean_name %in% c("Park Forest", "Chicago", "Winnetka", "Riverdale","Dolton"))), aes(y = nochange_av, x = C2median_av, color = "red"), size = 3) +
  ggrepel::geom_label_repel(data = (nochange %>% filter(clean_name %in% c("Park Forest", "Chicago", "Winnetka", "Riverdale", "Dolton"))), aes(y = (nochange_av), x = (C2median_av)), size = 3)+
  theme_classic() +
  scale_x_continuous(labels = scales::dollar, expand = c(0,0), limits = c(0,170000) ) +
  scale_y_continuous(labels = scales::dollar, expand = c(0,0), limits = c(0,170000) ) +
    theme(legend.position = "none") +

  labs(title = "Municipalities' Median AV & Breakeven Point - Class 2 Properties Only",
    y = "Breakeven Point", 
       x = "Median AV", 
 # caption = "The breakeven point is the assessed values at which a major class 2 property would not have their taxbill change if the 
 # GHE were eliminated. Excludes outliers: University Park, Bedford Park, McCook, Hodgkins, and Rosemont. Class 2 properties that had
 # claimed the GHE would have lower taxbills even if the GHE were removed if the AV is above the breakeven point."
 ) + coord_fixed()

Figure 2.3: Excludes outliers. The breakeven point is the assessed values at which a major class 2 property would not have their taxbill change if the GHE were eliminated. Excludes outliers: University Park, Bedford Park, McCook, Hodgkins, and Rosemont. Class 2 properties that had claimed the GHE would have lower taxbills even if the GHE were removed if the AV is above the breakeven point.

Code

nochange %>%
  filter(nochange_av < 300000) %>%
  ggplot(aes(x = nochange_av, 
             y = C2median_av, 
             label=clean_name
             )) +
  geom_point(aes(alpha = .5)) +
  
  geom_point(data = (nochange %>% filter(clean_name %in% c("Park Forest", "Chicago", "Winnetka", "Riverdale","Dolton"))), aes(x = nochange_av, y = C2median_av, color = "red"), size = 3) +
  ggrepel::geom_label_repel(data = (nochange %>% filter(clean_name %in% c("Park Forest", "Chicago", "Winnetka", "Riverdale", "Dolton"))), aes(x = (nochange_av), y = (C2median_av)), size = 3)+
  theme_classic() +
  scale_x_continuous(labels = scales::dollar, expand = c(0,0), limits = c(0,170000) ) +
  scale_y_continuous(labels = scales::dollar, expand = c(0,0), limits = c(0,170000) ) +
    theme(legend.position = "none") +

  labs(
    x = "Breakeven Point", 
       y = "Median AV", 
 #caption = "The breakeven point is the assessed values at which a major class 2 property would not have their taxbill change if the 
 #GHE were eliminated. Excludes outliers: University Park, Bedford Park, McCook, Hodgkins, and Rosemont. Class 2 properties that had
# claimed the GHE would have lower taxbills even if the GHE were removed if the AV is above the breakeven point."
) + coord_fixed()

Figure 2.4: Municipalities’ Median AV & Breakeven Point - Class 2 Properties Only

Figure 2.5: Do tax bills still counter-intuitively fall when exemptions are eliminated: The case of Phoenix, Park Forest, and Riverdale.

Code

nochange %>%
  filter(clean_name %in% c("Phoenix", "Park Forest", "Riverdale")) %>%
  ggplot(aes(y=nochange_av, 
             x = C2median_av, 
             label=clean_name
             )) +
  geom_point() +
  geom_text(aes(y = (nochange_av-500), x = (C2median_av-100)), size = 3)+
  theme_classic() +
  scale_x_continuous(labels = scales::dollar, limits = c(0,20000)) +
  scale_y_continuous(labels = scales::dollar, limits = c(0,20000)) +
  labs(#title = "Municipalities' Median AV & Breakeven Point",
    y = " Breakeven Point", 
       x = "Median AV", 
 caption = "") + coord_fixed()

(a) The breakeven point is the assessed values at which a major class 2 property would not have their taxbill change if the GHE were eliminated. Excludes outliers: University Park, Bedford Park, McCook, Hodgkins, and Rosemont. Class 2 properties that had claimed the GHE would have lower taxbills even if the GHE were removed if the AV is above the breakeven point.

Code

# nochange %>%
#       filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF RIVERDALE") ) %>%
#   filter(is.finite(nobillchange_propertyEAV) )%>%
#   select(agency_name, nobillchange_propertyEAV, rate_change, everything()) %>%
#  # filter(nobillchange_propertyEAV < 7000000) %>% 
#   ggplot(aes(x=nobillchange_propertyEAV, y = median_eav, label=clean_name)) +
#   geom_point()+
#   geom_text(aes(x =(nobillchange_propertyEAV), y = (median_eav+500)), size = 2) +
#       scale_x_continuous(labels = scales::dollar) +
#       scale_y_continuous(labels = scales::dollar) +
#   theme_classic() +
#   labs(x = "EAV of Class 2 Property w/ No Bill Change", y = "Median EAV of Municipality", title = "Can homeowners have lower tax bills if the GHE exemption was ended?",
#        subtitle = "Yes, in very, very rare cases. ")
# 
# 
# nochange %>%
#     #  filter(agency_name == "VILLAGE OF WINNETKA") %>%
#         filter(median_eav > 150000) %>%
#   filter(is.finite(nobillchange_propertyEAV) ) %>%
#   select(agency_name, nobillchange_propertyEAV, rate_change, everything()) %>%
#   #filter(nobillchange_propertyEAV < 7000000) %>% 
#   ggplot(aes(x=nobillchange_propertyEAV, y = median_eav, label=clean_name)) +
#   geom_point()+
#   geom_text(aes(x =(nobillchange_propertyEAV), y = (median_eav+5000)), size = 2) +
#       scale_x_continuous(labels = scales::dollar) +
#       scale_y_continuous(labels = scales::dollar) +
#   theme_classic() +
#   labs(x = "EAV of Class 2 Property w/ No Bill Change", y = "Median EAV of Municipality", title = "Can homeowners with GHE benefit from removal of the GHE?",
#        caption = "A $500K home in Winnetka would benefit if everyone else lost their General Homestead Exemption")


# nochange  %>%
#   filter(is.finite(nobillchange_propertyEAV) ) %>%
#   select(agency_name,   nobillchange_propertyEAV, rate_change, everything()) %>%
# #  filter(nobillchange_propertyEAV < 7000000) %>% 
#   ggplot(aes(x=nobillchange_propertyEAV, y = median_eav, label=clean_name)) +
#   # geom_smooth(method = "lm" )+
#   geom_text(aes(x = (nobillchange_propertyEAV), y = (median_eav+1000)), size = 2)+
#   theme_classic() +
#   scale_x_continuous(labels = scales::dollar) +
#   scale_y_continuous(labels = scales::dollar) +
#   labs(x = "EAV of Class 2 Property w/ No Bill Change", y = "Median EAV of Municipality", title = "Can homeowners who claim GHE benefit from removal of the GHE?",
#        subtitle = "In very, very rare instances: yes. ")

# 
# nochange %>%
#   select(agency_name,   nobillchange_propertyEAV, rate_change, everything()) %>%
# #  filter(nobillchange_propertyEAV < 7000000) %>% 
#   ggplot(aes(y=rate_change, x = median_eav, label=clean_name)) +
#   geom_point() +
#   theme_classic() +
#   scale_x_continuous(labels = scales::dollar) +
#   labs(x= "EAV of Class 2 Property w/ No Bill Change", y = "Composite Tax Rate Change (Percentage Points)", title = "EAV of Residential Property 'Breakeven Point' & Composite Tax Rate Change 
#        from Elimination of GHE")

nochange %>%
  select(agency_name,   nobillchange_propertyEAV, rate_change, everything()) %>%
  ggplot(aes(y=rate_change, x = C2median_av, label=clean_name)) +
  geom_point() +
  theme_classic() +
  scale_x_continuous(labels = scales::dollar) +
  labs(x= "AV of Class 2 Property w/ No Bill Change", y = "Composite Tax Rate Change (Percentage Points)", title = "AV of Residential Property 'Breakeven Point' & Composite Tax Rate Change 
       from Elimination of GHE")

Code

# 
# nochange %>%
#   select(agency_name,   nobillchange_propertyEAV, rate_change, everything()) %>%
#   ggplot(aes(y=rate_change, x = C2median_av*10, label=agency_name)) +
#   geom_point() +
#   theme_classic() +
#   scale_x_continuous(labels = scales::dollar) +
#   labs(x= "Market Value of Class 2 Property 
#        at 'Breakeven Point'", 
#        y = "Composite Tax Rate Change 
#        (Percentage Points)", 
#        title = "Market Value of Residential Property 'Breakeven Point' & 
#        Composite Tax Rate Change from Elimination of GHE")

2.2 Class 2 Summary Tables

Code

tax_bill_change_HO <- pin_data2 %>% 
  filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF WINNETKA", "VILLAGE OF RIVERDALE")) %>%
    filter(class >199 & class < 300) %>% 
  arrange(av) %>%
    mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            median_bill_cur = round(median(bill_current)),
            median_bill_new = round(median(bill_noexemps)),
            median_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe))

tax_bill_change_HO

Code

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF WINNETKA", "VILLAGE OF RIVERDALE")) %>%
    filter(class >199 & class < 300) %>% 
  arrange(av) %>%
  mutate(median_EAV = median(eav),
            median_bill_cur = round(median(bill_current)),
            median_bill_new = round(median(bill_noexemps)),
            median_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe))

Code

## Grouped by if they have a $0 tax bill and had the GHE per muni
pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF WINNETKA", "VILLAGE OF RIVERDALE")) %>%
  #filter(class == 203) %>% 
    filter(class >199 & class < 300) %>% 

  arrange(av) %>%
    mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, zero_bill, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            median_bill_cur = round(median(bill_current)),
            median_bill_new = round(median(bill_noexemps)),
            median_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe))

2.2.1 Calculation for Median properties in each munis

Code

## Grouped by if they have a $0 tax bill and had the GHE per muni
## Recalculating for Josh & Rachael
muni_median_summarytable <- pin_data2 %>% 
  # filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF WINNETKA", "VILLAGE OF RIVERDALE")) %>%
  
  #filter(class == 203) %>% 
  filter(class >199 & class < 300) %>% 
  
  # merge in muni residential median AV
  left_join(C2_munistats_filtered) %>%
  
  # +/- 500 from municpalities median residential AV
  filter(av+500 > median_av | av-500< median_av) %>%
  
  # Removes properties that received other types of exemptions
  filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0) %>%
  
  arrange(av) %>%
  
  mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  
  group_by(agency_name, # zero_bill, 
           has_HO_exemp
           ) %>% 
  
  summarize(AV = median(median_av),
            `Taxable EAV` = median(eav),
            bill_cur = round(median(bill_current)),
            bill_new = round(median(bill_noexemps)),
            bill_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe)) %>% 
  
  # merge in clean_names variable
  left_join(nicknames) %>%
  
  select(clean_name, has_HO_exemp, bill_cur, bill_new, bill_change, perceived_savings,  AV, `Taxable EAV`, everything())

2.3 Tax Bill Distributions

Code

pin_data2 %>%
  filter(major_class_code == 2 & bill_current < 30000 & agency_name == "VILLAGE OF PARK FOREST") %>% ggplot() +
  geom_histogram(aes(x= bill_current, bins = 100))+
    geom_vline(aes(xintercept=median(bill_current, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,20000), label = scales::dollar) +
  scale_y_continuous(limits = c(0, 1500), label = scales::comma)+
  labs(x="PF's Actual Tax Bills With Current Exemptions", y = "Number of Tax Bills")
pin_data2 %>%
  group_by(has_HO_exemp) %>%
  filter(major_class_code == 2 & bill_current < 30000 & agency_name == "VILLAGE OF PARK FOREST") %>% ggplot() +
  geom_histogram(aes(x= bill_current, bins = 100))+
    theme_classic()+theme(legend.position = "none") + 
  scale_x_continuous(limits = c(-5,20000), label = scales::dollar) +
  scale_y_continuous(limits = c(0, 1500), label = scales::comma)+
  labs(x="PF's Actual Tax Bills With Current Exemptions", y = "Number of Tax Bills") +
  facet_wrap(~has_HO_exemp)
pin_data2 %>%
  filter(major_class_code == 2 & bill_noexemps < 30000 & agency_name == "VILLAGE OF PARK FOREST") %>% ggplot() +
  geom_histogram(aes(x= bill_noexemps, bins = 100))+
    geom_vline(aes(xintercept=median(bill_noexemps, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,20000), label = scales::dollar) + 
    scale_y_continuous(limits = c(0, 1500), label = scales::comma)+

  labs(x="PF's Tax Bills Without General Homestead Exemptions", y = "Number of Tax Bills")

Code

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF PHOENIX") %>% ggplot() +
  geom_histogram(aes(x= bill_current, bins = 100))+
    geom_vline(aes(xintercept=median(bill_current, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,20000)) + 
   scale_y_continuous(limits = c(0, 600))+

  labs(title="PHOENIX's Current Tax Bill Distribution", x = "Dollars", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF PHOENIX") %>% ggplot() +
  geom_histogram(aes(x= bill_noexemps, bins = 100))+
    geom_vline(aes(xintercept=median(bill_noexemps, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,20000)) + 
    scale_y_continuous(limits = c(0, 600))+

  labs(title="PHOENIX's Tax Bills Without General Homestead Exemptions", y = "Number of Tax Bills")

Figure 2.6: ?(caption)

Figure 2.7: ?(caption)

Code

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF WINNETKA") %>% ggplot() +
  geom_histogram(aes(x= bill_current, bins = 100))+
    geom_vline(aes(xintercept=median(bill_current, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

 # scale_x_continuous(limits = c(-5,20000)) +
 # scale_y_continuous(limits = c(0, 80000)) +
  labs(title="WINNETKA's Actual Tax Bills With Current Exemptions", x= "Dollars", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2  & agency_name == "VILLAGE OF WINNETKA") %>% ggplot() +
  geom_histogram(aes(x= bill_noexemps, bins = 100))+
    geom_vline(aes(xintercept=median(bill_noexemps, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  #scale_x_continuous(limits = c(-5,20000)) + 
  #scale_y_continuous(limits = c(0, 80000)) +

  labs(title="WINNETKA's Tax Bills Without General Homestead Exemptions", x="Dollars", y = "Number of Tax Bills")

Figure 2.8: ?(caption)

Figure 2.9: ?(caption)

Code

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF PARK FOREST") %>% 
  ggplot() +
  geom_histogram(aes(x= bill_change, bins = 100))+
    geom_vline(aes(xintercept=median(bill_change, na.rm=TRUE), color = "red")) +
    theme_classic()+
  theme(legend.position = "none") + 
    scale_x_continuous(limits = c(-10000,10000)) +

#   scale_y_continuous(limits = c(0, 2000))+
  labs(title="Park Forest's Change in Tax Bills", subtitle =  "Hypothetical with no General Homestead Exemptions", x = "Change in Tax Bill ($)", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF PHOENIX") %>% ggplot() +
  geom_histogram(aes(x= bill_change, bins = 100))+
    geom_vline(aes(xintercept=median(bill_change, na.rm=TRUE), color = "red")) +
    theme_classic()+ theme(legend.position = "none") + 
   scale_x_continuous(limits = c(-10000,10000)) +

#   scale_x_continuous(limits = c(-2800,2800)) + 
#   scale_y_continuous(limits = c(0, 2000))+

  labs(title ="PHOENIX's Change in Tax Bills", subtitle = "Hypothetical with no General Homestead Exemptions", x = "Change in Tax Bill ($)", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF WINNETKA") %>% ggplot() +
  geom_histogram(aes(x= bill_change, bins = 100))+
    geom_vline(aes(xintercept=median(bill_change, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 
   scale_x_continuous(limits = c(-10000,10000)) +
  labs(title="WINNETKA's Change in Tax Bills Without General Homestead Exemptions", y = "Number of Tax Bills", x = "Dollars")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF RIVERDALE") %>% ggplot() +
  geom_histogram(aes(x= bill_change, bins = 100))+
    geom_vline(aes(xintercept=median(bill_change, na.rm=TRUE), color = "red")) +
    theme_classic()+
  theme(legend.position = "none") + 
   scale_x_continuous(limits = c(-10000,10000)) +
labs(title="RIVERDALE's Change in Tax Bills Without General Homestead Exemptions", y = "Number of Tax Bills", x = "Dollars")

Figure 2.10: ?(caption)

Figure 2.11: ?(caption)

Figure 2.12: ?(caption)

Figure 2.13: ?(caption)

Code

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF WINNETKA")) %>%
  filter(class >199 & class < 300) %>% 
  arrange(av) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, zero_bill, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            mean_bill_cur = mean(bill_current, na.rm=TRUE),
            median_bill_cur = median(bill_current),
            mean_bill_new = mean(bill_noexemps, na.rm=TRUE),
            median_bill_new = median(bill_noexemps),
            mean_change = mean(bill_change, na.rm=TRUE),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)
pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF WINNETKA", "VILLAGE OF RIVERDALE")) %>%
  filter(class >199 & class < 300) %>% 
  arrange(av) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            mean_bill_cur = mean(bill_current, na.rm=TRUE),
            median_bill_cur = median(bill_current),
            mean_bill_new = mean(bill_noexemps, na.rm=TRUE),
            median_bill_new = median(bill_noexemps),
            mean_change = mean(bill_change, na.rm=TRUE),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)
pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("VILLAGE OF PARK FOREST", "VILLAGE OF PHOENIX", "VILLAGE OF WINNETKA","VILLAGE OF RIVERDALE")) %>%
  filter(class >199 & class < 300) %>% 
  arrange(av) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            mean_bill_cur = mean(bill_current, na.rm=TRUE),
            median_bill_cur = median(bill_current),
            mean_bill_new = mean(bill_noexemps, na.rm=TRUE),
            median_bill_new = median(bill_noexemps),
            mean_change = mean(bill_change, na.rm=TRUE),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

?(caption)

---

1. \(\sum(EAV_{exempt} * taxrate_{current})\) = $1.579 billion

   \(\sum(EAV_{exempt} * taxrate_{hyp})\) = $1.385 billion